Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

FPGA implementation of an adaptive window size image impulse noise suppression system

  • Original Research Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

The conventional method for image impulse noise suppression is standard median filter utilization, which is satisfying for low noise densities, but not for medium to high noise densities. Adding a noise detection step, as proposed in the literature, makes this algorithm suitable for higher noises, but may degrade the performance at low noise densities. An adaptive switching median-based (ASM) algorithm has been used in this paper for noise suppression. First, the algorithm is modified to achieve a higher PSNR, especially for low noise densities. Then, the structure of the modified algorithm is improved to obtain higher operating speed in hardware implementation, for real-time applications. The implemented algorithm works in two steps, detection and filtering. The noise detection method is enhanced, by merging the amount of memory used for the algorithm implementation. As a result, less hardware resources are required, while the chance of false noise detection is reduced, due to the improvement made in the algorithm. In the filtering step, an adaptive window size is used, based on the measured noise density. This improved algorithm is adopted for more efficient hardware implementation. In addition, high parallelism is utilized to boost the operating frequency, and meanwhile, clock gating is used to lower power consumption. This architecture, then, has been implemented physically on an FPGA, and an operating frequency of 93 MHz is achieved. The hardware requirement is approximately 10,000 4-input LUTs, and the processing time for a 512 × 512 pixels image is measured at 12 ms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. CCD Image Sensor Noise Sources: Application Note, 1st edn. Eastman Kodak Company, Rochester (2001)

    Google Scholar 

  2. Cumani, C.: Introduction to CCDs. In: Neutron Radiography, Fifth International Topical Meeting on Garching (2004)

  3. Varghese, Gijesh, Wang, Zhou: Video denoising based on a spatiotemporal Gaussian scale mixture model. IEEE Trans. Circuits Syst. Video Technol. 20, 1032–1040 (2010)

    Article  Google Scholar 

  4. Jin, L., et al.: Quaternion-based impulse noise removal from color video sequences. IEEE Trans. Circuits Syst. Video Technol. 23, 741–755 (2013)

    Article  Google Scholar 

  5. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice-Hall Inc, New Jersey (2002)

    Google Scholar 

  6. Guo, Q., et al.: An efficient SVD-based method for image denoising. IEEE Trans. Circuits Syst. Video Technol. 26, 868–880 (2016)

    Article  Google Scholar 

  7. Yang, J., et al.: Image and video denoising using adaptive dual-tree discrete wavelet packets. IEEE Trans. Circuits Syst. Video Technol. 19, 642–655 (2009)

    Article  Google Scholar 

  8. Malinski, Lukasz, Smolka, Bogdan: Fast averaging peer group filter for the impulsive noise removal in color images. J. Real-Time Image Proc. 11, 427–444 (2016)

    Article  Google Scholar 

  9. Malinski, L., Bogdan S.: Fast adaptive switching technique of impulsive noise removal in color images. J. Real-Time Image Process. 1–22 (2016). doi:10.1007/s11554-016-0599-6

    Article  Google Scholar 

  10. Chatterjee, P., Milanfar, P.: Is denoising dead? Image Process. IEEE Trans. 19(4), 895–911 (2010)

    Article  MathSciNet  Google Scholar 

  11. Ko, S.-J., Lee, Y.H.: Center weighted median filters and their applications to image enhancement. Circuits Syst. IEEE Trans. 38, 984–993 (1991)

    Article  Google Scholar 

  12. Wang, Z., Zhang, D.: Progressive switching median filter for the removal of impulse noise from highly corrupted images. Circuits Syst II Analog Digit. Signal Process. IEEE Trans. 46, 78–80 (1999)

    Article  Google Scholar 

  13. Windyga, P.S.: Fast impulsive noise removal. Image Process. IEEE Trans. 10, 173–179 (2001)

    Article  Google Scholar 

  14. Chen, T., Ma, K.-K., Chen, L.-H.: Tri-state median filter for image denoising. Image Process. IEEE Trans. 8, 1834–1838 (1999)

    Article  Google Scholar 

  15. Fabijanska, A., Sankowski, D.: Noise adaptive switching median-based filter for impulse noise removal from extremely corrupted images. Image Process. IET 5(5), 472–480 (2011)

    Article  Google Scholar 

  16. Eng, H.L., Ma, K.K.: Noise adaptive soft-switching median filter. Image Process. IEEE Trans. 10(2), 242–251 (2001)

    Article  Google Scholar 

  17. Andreadis, I., Louverdis, G.: Real-time adaptive image impulse noise suppression. Instrum. Meas. IEEE Trans. 53(3), 798–806 (2004)

    Article  Google Scholar 

  18. Hwang, H., Haddad, R.: Adaptive median filters: new algorithms and results. Image Process. IEEE Trans. 4(4), 499–502 (1995)

    Article  Google Scholar 

  19. Hu, Y., Ji, H.: Research on image median filtering algorithm and its FPGA implementation. In: Intelligent Systems. GCIS’09. WRI Global Congress on IEEE (2009)

  20. Nieminen, A., Neuvo, Y.: Comments on theoretical analysis of the max/median filter” by GR Arce and MP McLaughlin. Acoust Speech Signal Process. IEEE Trans. 36, 826–827 (1988)

    Article  Google Scholar 

  21. Baddar, S.W.A.H., Batcher, K.E.: Designing Sorting Networks: A New Paradigm. Springer, New York (2012)

    Google Scholar 

  22. Jayanthi Sree, S., Ashwin, S., Aravind Kumar, S.: Edge preserving algorithm for impulse noise removal using FPGA. In: Machine Vision and Image Processing (MVIP), 2012 International Conference on IEEE (2012)

  23. Matsubara, T., Moshnyaga, V.G., Hashimoto, K.: A FPGA implementation of low-complexity noise removal. In: Electronics, Circuits, and Systems (ICECS), 17th IEEE International Conference on IEEE (2010)

  24. Kamarujjaman, M.M., Maitra, M.: An efficient FPGA based de-noising architecture for removal of high density impulse noise in images. In: IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) (2015)

  25. Mukherjee, M., Maitra, M.: Reconfigurable architecture of adaptive median filter—an FPGA based approach for impulse noise suppression. In: Computer, Communication, Control and Information Technology (C3IT), 2015 IEEE Third International Conference, pp. 1–6

  26. Anand, M., Narasimha, Y.: Removal of salt and pepper noise from highly corrupted images using mean deviation statistical parameter. Int. J. Comput. Sci. Eng. 5(2), 113–119 (2013)

    Google Scholar 

  27. Fahmy, S.A., Cheung, P.Y., Luk, W.: Novel FPGA-based implementation of median and weighted median filters for image processing. In: Field Programmable Logic and Applications, International Conference, pp. 142–147 (2005)

  28. Shi, P., Ward, R.K.: A neural network implementation of the median filter. In: Communications, Computers and Signal Processing, IEEE Pacific Rim Conference, pp. 513–516 (1989)

  29. Gil, J., Werman, M.: Computing 2-D min, median, and max filters. In: Pattern Analysis and Machine Intelligence, IEEE Transactions, pp. 504–507 (1993)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran

    Parham Taghinia Jelodari, Mojtaba Parsa Kordasiabi, Samad Sheikhaei & Behjat Forouzandeh

Authors
  1. Parham Taghinia Jelodari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mojtaba Parsa Kordasiabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Samad Sheikhaei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Behjat Forouzandeh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Samad Sheikhaei.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Taghinia Jelodari, P., Parsa Kordasiabi, M., Sheikhaei, S. et al. FPGA implementation of an adaptive window size image impulse noise suppression system. J Real-Time Image Proc 16, 2015–2026 (2019). https://doi.org/10.1007/s11554-017-0705-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-017-0705-4

Keywords

Search

Navigation